FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge
Not on the map. Why?
Abstract (arXiv)
The field of Automatic Facial Expression Analysis has grown rapidly in recent years. However, despite progress in new approaches as well as benchmarking efforts, most evaluations still focus on either posed expressions, near-frontal recordings, or both. This makes it hard to tell how existing expression recognition approaches perform under conditions where faces appear in a wide range of poses (or camera views), displaying ecologically valid expressions. The main obstacle for assessing this is the availability of suitable data, and the challenge proposed here addresses this limitation. The FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to the estimation of Action Units occurrence and intensity under different camera views. In this paper we present the third challenge in automatic recognition of facial expressions, to be held in conjunction with the 12th IEEE conference on Face and Gesture Recognition, May 2017, in Washington, United States. Two sub-challenges are defined: the detection of AU occurrence, and the estimation of AU intensity. In this work we outline the evaluation protocol, the data used, and the results of a baseline method for both sub-challenges.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Feb 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 0; 2023: 2; 2024: 2; 2025: 0; 2026: 2.
9 papers.
- 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
- 2024Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkitcited by 16
- 2021Graph-based Facial Affect Analysis: A Reviewcited by 14
- 2024On the representation and methodology for wide and short range head pose estimationcited by 11
- 2023How far generated data can impact Neural Networks performance?cited by 1
- 2021Uncover Common Facial Expressions in Terracotta Warriors: A Deep Learning Approachcited by 0
- 2023FERGI: Automatic Scoring of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression Reactioncited by 0
- 2026Facial-Expression-Aware Prompting for Empathetic LLM Tutoringcited by 0
- 2026VISAFF: Speaker-Centered Visual Affective Feature Learning for Emotion Recognition in Conversationcited by 0
- 2026VISAFF: Speaker-Centered Visual Affective Feature Learning for Emotion Recognition in Conversation18 May 2026
- 2026Facial-Expression-Aware Prompting for Empathetic LLM Tutoring10 Mar 2026
- 2024Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit26 Sep 2024
- 2024On the representation and methodology for wide and short range head pose estimation11 Jan 2024
- 2023FERGI: Automatic Scoring of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression Reaction5 Dec 2023
- 2023How far generated data can impact Neural Networks performance?27 Mar 2023
- 2021Uncover Common Facial Expressions in Terracotta Warriors: A Deep Learning Approach11 May 2021
- 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study8 May 2021
- 2021Graph-based Facial Affect Analysis: A Review29 Mar 2021